Recognising lepidopteran images based on locality-constrained linear coding and SVM

In this paper, a novel lepidopteran insect images recognition method is proposed. The captured insect images are first preprocessed to separate the foreground from the complicated background. Then, two wings are cut out from the insect, calibrated and segmented into a number of superpixels. The values of l, a, b, x, y of each superpixel are calculated as feature descriptors. Those descriptors are encoded with locality-constrained linear coding and then pooled into a feature vector. Finally, the multi-class linear support vector machine is used for classification. The approach is testified in a data-set including 576 images of insect specimens from 10 different lepidopteran species and the recognition accuracy is over 99% in average. The experimental results suggested that the proposed method performs well in lepidopteran insect species recognition.